R ile analize başlarken¹

1 R nerede kullanılır

• Veri düzenleme
• İstatistik analiz
• Web sayfası hazırlama (Statik/Dinamik)
• Sunum hazırlama
• Programlama
• Otomatik, periodik ve tekrarlanabilir rapor hazırlama
• pdf, html, ppt oluşturma
• tez yazma
• kitap yazma
• rapor şablonu oluşturma
• …

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2 R generation

R yıllar içinde çok fazla değişim gösterdi

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

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3 R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

3.1 R-project

https://cran.r-project.org/

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3.2 RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

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3.2.1 RStudio eklentileri

• Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

devtools::install_github("rstudio/addinexamples", type = "source")

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4 MacOS için

4.1 X11

https://www.xquartz.org/

4.2 Java OS

https://support.apple.com/kb/dl1572

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5 R zor şeyler için kolay, kolay şeyler için zor

• R makes easy things hard, and hard things easy

• Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf

#RStats — There are always several ways to do the same thing… nice example on with the identity matrix by @TeaStats https://t.co/O3GXdPiM32

— Colin Fay 🤘 (@_ColinFay) April 1, 2019

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6 R paketleri

6.1 Neden paketler var

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I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.

— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

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https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

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6.2 Paketleri nereden bulabiliriz

• Available CRAN Packages By Name
  https://cran.r-project.org/web/packages/available_packages_by_name.html

• CRAN Task Views
  https://cran.r-project.org/web/views/

• Bioconductor
  https://www.bioconductor.org

• RecommendR
  http://recommendr.info/

• pkgsearch
  CRAN package search
  https://github.com/metacran/pkgsearch

• CRANsearcher
  https://github.com/RhoInc/CRANsearcher

• Awesome R
  https://awesome-r.com/

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6.3 Kendi paket evrenini oluştur

• pkgverse: Build a Meta-Package Universe
  https://cran.r-project.org/web/packages/pkgverse/index.html

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6.4 R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")

# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)

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7 R için yardım bulma

# ?mean
# ??efetch
# help(merge)
# example(merge)

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• Vignette

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• RDocumentation https://www.rdocumentation.org

• R Package Documentation https://rdrr.io/

• GitHub

• Stackoverflow

https://stackoverflow.com/

• Google uygun anahtar kelime

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How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr

— Emily Bovee (@ebovee09) August 10, 2018

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• Google’da ararken [R] yazmak da işe yarayabiliyor.

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• searcher package 📦

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• Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

• Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/

• Twitter

https://twitter.com/hashtag/rstats?src=hash

• Reproducible Examples

Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX

— ZhiYang (@zhiiiyang) October 18, 2018

• Keeping up to date with R news
  https://masalmon.eu/2019/01/25/uptodate/

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8 R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

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9 RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

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9.1 Excel

9.2 SPSS

9.3 CSV

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10 Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?

— Leonard Kiefer (@lenkiefer) July 7, 2018

# library(nycflights13)
# summary(flights)

View(data)

data

head

tail

glimpse

str

skimr::skim()

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11 Veriyi değiştirme

11.1 Veriyi kod ile değiştirelim

11.2 Veriyi eklentilerle değiştirme

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11.3 RStudio aracılığıyla recode

questionr paketi kullanılacak

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https://juba.github.io/questionr/articles/recoding_addins.html

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12 Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

Parsed with column specification:
cols(
  Sepal.Length = col_double(),
  Sepal.Width = col_double(),
  Petal.Length = col_double(),
  Petal.Width = col_double(),
  Species = col_character()
)

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)

 DESCRIPTIVES

 Descriptives                                          
 ───────────────────────────────────────────────────── 
                          Species       Sepal.Length   
 ───────────────────────────────────────────────────── 
   N                      setosa                  50   
                          versicolor              50   
                          virginica               50   
   Missing                setosa                   0   
                          versicolor               0   
                          virginica                0   
   Mean                   setosa                5.01   
                          versicolor            5.94   
                          virginica             6.59   
   Std. error mean        setosa              0.0498   
                          versicolor          0.0730   
                          virginica           0.0899   
   Median                 setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   Mode                   setosa                5.00   
                          versicolor            5.50   
                          virginica             6.30   
   Sum                    setosa                 250   
                          versicolor             297   
                          virginica              329   
   Standard deviation     setosa               0.352   
                          versicolor           0.516   
                          virginica            0.636   
   Variance               setosa               0.124   
                          versicolor           0.266   
                          virginica            0.404   
   Range                  setosa                1.50   
                          versicolor            2.10   
                          virginica             3.00   
   Minimum                setosa                4.30   
                          versicolor            4.90   
                          virginica             4.90   
   Maximum                setosa                5.80   
                          versicolor            7.00   
                          virginica             7.90   
   Skewness               setosa               0.120   
                          versicolor           0.105   
                          virginica            0.118   
   Std. error skewness    setosa               0.337   
                          versicolor           0.337   
                          virginica            0.337   
   Kurtosis               setosa              -0.253   
                          versicolor          -0.533   
                          virginica           0.0329   
   Std. error kurtosis    setosa               0.662   
                          versicolor           0.662   
                          virginica            0.662   
   25th percentile        setosa                4.80   
                          versicolor            5.60   
                          virginica             6.23   
   50th percentile        setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   75th percentile        setosa                5.20   
                          versicolor            6.30   
                          virginica             6.90   
 ───────────────────────────────────────────────────── 

---

# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

12.1 summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

library(summarytools)

Registered S3 method overwritten by ‘pryr’: method from print.bytes Rcpp

summarytools::freq(iris$Species, style = "rmarkdown")

12.1.1 Frequencies

12.1.1.1 iris$Species

Type: Factor

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", headings = FALSE)

with(tobacco, print(ctable(smoker, diseased), method = 'render'))

Cross-Tabulation, Row Proportions

smoker * diseased

Data Frame: tobacco

	diseased
smoker	Yes				No				Total
Yes	125	(	41.9%	)	173	(	58.1%	)	298	(	100.0%	)
No	99	(	14.1%	)	603	(	85.9%	)	702	(	100.0%	)
Total	224	(	22.4%	)	776	(	77.6%	)	1000	(	100.0%	)

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))

‘omit.headings’ will disappear in future releases; use ‘headings’ instead

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

summarytools::descr(iris, style = "rmarkdown")

Non-numerical variable(s) ignored: Species ### Descriptive Statistics
#### iris
N: 150

	Petal.Length	Petal.Width	Sepal.Length	Sepal.Width
Mean	3.76	1.20	5.84	3.06
Std.Dev	1.77	0.76	0.83	0.44
Min	1.00	0.10	4.30	2.00
Q1	1.60	0.30	5.10	2.80
Median	4.35	1.30	5.80	3.00
Q3	5.10	1.80	6.40	3.30
Max	6.90	2.50	7.90	4.40
MAD	1.85	1.04	1.04	0.44
IQR	3.50	1.50	1.30	0.50
CV	0.47	0.64	0.14	0.14
Skewness	-0.27	-0.10	0.31	0.31
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-1.42	-1.36	-0.61	0.14
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

descr(iris, 
      stats = c("mean", "sd", "min", "med", "max"), 
      transpose = TRUE, 
      headings = FALSE, style = "rmarkdown")

Non-numerical variable(s) ignored: Species

	Mean	Std.Dev	Min	Median	Max
Petal.Length	3.76	1.77	1.00	4.35	6.90
Petal.Width	1.20	0.76	0.10	1.30	2.50
Sepal.Length	5.84	0.83	4.30	5.80	7.90
Sepal.Width	3.06	0.44	2.00	3.00	4.40

# view(dfSummary(iris))

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")

text graphs are displayed; set ‘tmp.img.dir’ parameter to activate png graphs NAs introduced by coercion### Data Frame Summary
#### tobacco
Dimensions: 1000 x 9
Duplicates: 2

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
1	gender [factor]	1. F 2. M	489 (50.0%) 489 (50.0%)	IIIIIIIIII IIIIIIIIII	978 (97.8%)	22 (2.2%)
2	age [numeric]	Mean (sd) : 49.6 (18.3) min < med < max: 18 < 50 < 80 IQR (CV) : 32 (0.4)	63 distinct values	.     .     . . . : : : : : : . : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :	975 (97.5%)	25 (2.5%)
3	age.gr [factor]	1. 18-34 2. 35-50 3. 51-70 4. 71 +	258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%)	IIIII IIII IIIIII III	975 (97.5%)	25 (2.5%)
4	BMI [numeric]	Mean (sd) : 25.7 (4.5) min < med < max: 8.8 < 25.6 < 39.4 IQR (CV) : 5.7 (0.2)	974 distinct values	:         : : :         : : :       : : : : :     . : : : : : .	974 (97.4%)	26 (2.6%)
5	smoker [factor]	1. Yes 2. No	298 (29.8%) 702 (70.2%)	IIIII IIIIIIIIIIIIII	1000 (100%)	0 (0%)
6	cigs.per.day [numeric]	Mean (sd) : 6.8 (11.9) min < med < max: 0 < 0 < 40 IQR (CV) : 11 (1.8)	37 distinct values	: : : : :   . . . . . .	965 (96.5%)	35 (3.5%)
7	diseased [factor]	1. Yes 2. No	224 (22.4%) 776 (77.6%)	IIII IIIIIIIIIIIIIII	1000 (100%)	0 (0%)
8	disease [character]	1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ]	36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.5%)	III III I I I I I I I I	222 (22.2%)	778 (77.8%)
9	samp.wgts [numeric]	Mean (sd) : 1 (0.1) min < med < max: 0.9 < 1 < 1.1 IQR (CV) : 0.2 (0.1)	0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded	IIIII IIII IIIIII III	1000 (100%)	0 (0%)

# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")

Non-numerical variable(s) ignored: Species ### Descriptive Statistics
#### iris
Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	1.46	0.17	1.00	1.50	1.90
Petal.Width	0.25	0.11	0.10	0.20	0.60
Sepal.Length	5.01	0.35	4.30	5.00	5.80
Sepal.Width	3.43	0.38	2.30	3.40	4.40

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80

# view(iris_stats_by_species)

---

data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

12.1.2 Descriptive Statistics

12.1.2.1 BMI by age.gr

Data Frame: tobacco
N: 258

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

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BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

‘omit.headings’ will disappear in future releases; use ‘headings’ instead

	Mean	Std.Dev	Min	Median	Max
18-34	23.84	4.23	8.83	24.04	34.84
35-50	25.11	4.34	10.35	25.11	39.44
51-70	26.91	4.26	9.01	26.77	39.21
71 +	27.45	4.37	16.36	27.52	38.37

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tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')

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what.is(iris)

function ‘is’ appears not to be S3 generic; found functions that look like S3 methods‘>=’ not meaningful for factors$properties

$attributes.lengths names class row.names 5 1 150

$extensive.is [1] “is.data.frame” “is.list” “is.object”
[4] “is.recursive” “is.unsorted”

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freq(tobacco$gender, style = 'rmarkdown')

### Frequencies  
#### tobacco$gender  
**Type:** Factor  

|       | Freq | % Valid | % Valid Cum. | % Total | % Total Cum. |
|-----------:|-----:|--------:|-------------:|--------:|-------------:|
|      **F** |  489 |   50.00 |        50.00 |   48.90 |        48.90 |
|      **M** |  489 |   50.00 |       100.00 |   48.90 |        97.80 |
| **\<NA\>** |   22 |         |              |    2.20 |       100.00 |
|  **Total** | 1000 |  100.00 |       100.00 |  100.00 |       100.00 |

---

print(freq(tobacco$gender), method = 'render')

Frequencies

tobacco$gender

Type: Factor

		Valid		Total
gender	Freq	%	% Cum.	%	% Cum.
F	489	50.00	50.00	48.90	48.90
M	489	50.00	100.00	48.90	97.80
<NA>	22			2.20	100.00
Total	1000	100.00	100.00	100.00	100.00

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

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12.2 skimr

library(skimr)
skim(df)

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12.3 DataExplorer

library(DataExplorer)
DataExplorer::create_report(df)

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12.4 inspectdf

https://github.com/alastairrushworth/inspectdf

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12.5 Grafikler

# library(ggplot2)
# library(mosaic)
# mPlot(irisdata)

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ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')

12.5.1 Cross-Tabulation, Row Proportions

12.5.1.1 gender * smoker

Data Frame: tobacco

	smoker	Yes	No	Total
gender
F		147 (30.1%)	342 (69.9%)	489 (100.0%)
M		143 (29.2%)	346 (70.8%)	489 (100.0%)
<NA>		8 (36.4%)	14 (63.6%)	22 (100.0%)
Total		298 (29.8%)	702 (70.2%)	1000 (100.0%)

---

print(ctable(tobacco$gender, tobacco$smoker), method = 'render')

Cross-Tabulation, Row Proportions

gender * smoker

Data Frame: tobacco

	smoker
gender	Yes				No				Total
F	147	(	30.1%	)	342	(	69.9%	)	489	(	100.0%	)
M	143	(	29.2%	)	346	(	70.8%	)	489	(	100.0%	)
<NA>	8	(	36.4%	)	14	(	63.6%	)	22	(	100.0%	)
Total	298	(	29.8%	)	702	(	70.2%	)	1000	(	100.0%	)

Generated by summarytools 0.9.3 (R version 3.5.3)
2019-04-18

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

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Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh

— Gina Reynolds (@EvaMaeRey) August 13, 2018

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Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv

— Radoslaw Panczak (@RPanczak) October 2, 2018

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12.6 Tablolar

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13 Bazı arayüzler

Link

13.1 Rcmdr

library(Rcmdr)

Rcmdr::Commander()

• A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

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13.2 jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

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14 R nereden öğrenilir

https://sbalci.github.io/MyRCodesForDataAnalysis/WhereToLearnR.nb.html

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15 Sonraki Konular

• RStudio ile GitHub
• Hipotez testleri
• R Markdown ve R Notebook ile tekrarlanabilir rapor

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16 Geri Bildirim

• Geri bildirim için tıklayınız: Geri bildirim formu

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# Save Final Data

saved data after analysis to `Data-After-Analysis.xlsx`.

saveRDS(mydata, "Data-After-Analysis.rds")

writexl::write_xlsx(mydata, "Data-After-Analysis.xlsx")

file.info("Data-After-Analysis.xlsx")$ctime

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17 Libraries Used

citation()

citation("tidyverse")
citation("foreign")
citation("tidylog")
citation("janitor")
citation("jmv")
citation("tangram")
citation("finalfit")
citation("summarytools")
citation("ggstatplot")
citation("readxl")

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report::cite_packages(session = sessionInfo())

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sessionInfo()

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18 Notes

Completed on 2019-09-21 17:39:32.

Serdar Balci, MD, Pathologist
drserdarbalci@gmail.com
https://rpubs.com/sbalci/CV

https://sbalci.github.io/
https://github.com/sbalci
—

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1. Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩︎